Privacy-Preserving Chaotic Extreme Learning Machine with Fully Homomorphic Encryption
The Machine Learning and Deep Learning Models require a lot of data for the training process, and in some scenarios, there might be some sensitive data, such as customer information involved, which the organizations might be hesitant to outsource for model building. Some of the privacy-preserving techniques such as Differential Privacy, Homomorphic Encryption, and Secure Multi-Party Computation can be integrated with different Machine Learning and Deep Learning algorithms to provide security to the data as well as the model. In this paper, we propose a Chaotic Extreme Learning Machine and its encrypted form using Fully Homomorphic Encryption where the weights and biases are generated using a logistic map instead of uniform distribution. Our proposed method has performed either better or similar to the Traditional Extreme Learning Machine on most of the datasets.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningDeep LearningPrivacy PreservingSimilar Papers 제목 키워드 기반
A chaotic maps-based privacy-preserving distributed deep learning for incomplete and Non-IID datasets
Federated Learning is a machine learning approach that enables the training of a deep learning model among several participants with sensitive data that wish to share their own knowledge without compromising the privacy …
Deep LearningFederated LearningPrivacy PreservingEncrypted statistical machine learning: new privacy preserving methods
We present two new statistical machine learning methods designed to learn on fully homomorphic encrypted (FHE) data. The introduction of FHE schemes following Gentry (2009) opens up the prospect of privacy preserving sta…
BIG-bench Machine LearningGeneral ClassificationPrivacy PreservingUsing Echo-State Networks to Reproduce Rare Events in Chaotic Systems
We apply the Echo-State Networks to predict the time series and statistical properties of the competitive Lotka-Volterra model in the chaotic regime. In particular, we demonstrate that Echo-State Networks successfully le…
Time SeriesA physics-aware machine to predict extreme events in turbulence
We propose a physics-aware machine learning method to time-accurately predict extreme events in a turbulent flow. The method combines two radically different approaches: empirical modelling based on reservoir computing, …
Secure Multi-Party Computation Based Privacy Preserving Extreme Learning Machine Algorithm Over Vertically Distributed Data
Especially in the Big Data era, the usage of different classification methods is increasing day by day. The success of these classification methods depends on the effectiveness of learning methods. Extreme learning machi…
ClassificationGeneral ClassificationPrivacy Preserving